Cost-Effective Parkinson’s Disease Diagnosis Through IoT-Based Finger Tapping and Real-Time Machine Learning Classification

Dwi Arraziqi; Tri Arief Sardjono; Mauridhi Hery Purnomo

doi:10.23887/janapati.v14i1.86371

Authors

Dwi Arraziqi Institut Teknologi Sepuluh Nopember
Tri Arief Sardjono Institut Teknologi Sepuluh Nopember
Mauridhi Hery Purnomo Institut Teknologi Sepuluh Nopember

DOI:

https://doi.org/10.23887/janapati.v14i1.86371

Keywords:

Finger tapping analysis, IoT, machine learning, Parkinson’s disease, peak amplitude, telemedicine

Abstract

Parkinson's disease (PD) is a progressive neurological condition that significantly impacts motor functions, including finger tapping (FT). This study aims to develop a cost-effective, real-time, easily implementable, IoT-enabled electronic health record (EHR)-integrated FT analysis system capable of remotely detecting PD with high accuracy. The study investigates the use of peak amplitude, the Internet of Things (IoT), and various machine learning classifiers to detect PD through FT pattern analysis on a smartphone application. K-Nearest Neighbors, Convolutional Neural Networks, Support Vector Machines, and Logistic Regression exhibited 100% accuracy, while Naïve Bayes and Decision Trees (DT) had accuracies ranging from 71% to 92%. All classifiers had an Area Under the Curve (AUC) value of 1, except DT with an AUC value of 0.75. Data from healthy control (HC) and PD groups showed normal distribution, as determined by the Shapiro-Wilk test (p > 0.05), except for the HC group at peak 10. Significant differences were found between the PD and HC groups, as evidenced by the T-test and U-test (p < 0.05), with Pearson's r <= -0.79 and Spearman's rho = -0.58, indicating strong agreement between machine learning classification and neurologist evaluation. The proposed IoT-based approach demonstrated high diagnostic accuracy, cost-effectiveness, real-time monitoring capabilities, easy implementation, scalability for telemedicine, and accessibility to EHR during the COVID-19 pandemic. Future studies will focus on expanding the dataset.

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